Latent Common Return Volatility Factors: Capturing Elusive Predictive Accuracy Gains When Forecasting Volatility

44 Pages Posted: 14 Jul 2017

See all articles by Mingmian Cheng

Mingmian Cheng

Department of Finance, Lingnan (University) College, Sun Yat-sen University

Norman R. Swanson

Rutgers University - Department of Economics; Rutgers, The State University of New Jersey - Department of Economics

Xiye Yang

Rutgers, The State University of New Jersey - Department of Economics

Date Written: June 20, 2017

Abstract

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns dataset with 267 constituents of the S&P 500 index. In the first step, we apply either LASSO or elastic net shrinkage on estimates of integrated volatility of all constituents in the dataset, in order to select a subset of asset return series for further processing. In the second step, we utilize (sparse) principal component analysis to estimate latent common asset return factors, from which latent integrated volatility factors are extracted. Although we find limited in-sample fit improvement, relative to a benchmark HAR model, all of our proposed factor-augmented models result in substantial out-of-sample predictive accuracy improvement. In particular, forecasting gains are observed at market, sector, and individual-stock levels, with the exception of the financial sector. Further investigation of the factor structures for non-financial assets shows that industrial and technology stocks are characterized by minimal exposure to financial assets, inasmuch as forecasting gains associated with factor-augmented models for these types of assets are largely attributable to the inclusion of non-financial stock price return volatility in our latent factors.

Keywords: Forecasting, Latent common volatility factor, Dimension reduction, Factor-augmented regression, High-frequency data, High-dimensional data

JEL Classification: C22, C52, C53, C58

Suggested Citation

Cheng, Mingmian and Swanson, Norman Rasmus and Swanson, Norman Rasmus and Yang, Xiye, Latent Common Return Volatility Factors: Capturing Elusive Predictive Accuracy Gains When Forecasting Volatility (June 20, 2017). Available at SSRN: https://ssrn.com/abstract=2998304 or http://dx.doi.org/10.2139/ssrn.2998304

Mingmian Cheng

Department of Finance, Lingnan (University) College, Sun Yat-sen University ( email )

135 Xingang West Road
Haizhu District
Guangzhou, Guangdong 510275
China

Norman Rasmus Swanson (Contact Author)

Rutgers University - Department of Economics ( email )

NJ
United States

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Rutgers, The State University of New Jersey - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States
848-932-7432 (Phone)

HOME PAGE: http://econweb.rutgers.edu/nswanson/

Xiye Yang

Rutgers, The State University of New Jersey - Department of Economics ( email )

75 Hamilton Street
New Brunswick, NJ 08901
United States

HOME PAGE: http://economics.rutgers.edu/people/474-xiye-yang

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